Examples with DoubleFeature com.joliciel.talismane.machineLearning.features.DoubleFeature used on opensource projects

Example 1 with DoubleFeature

use of com.joliciel.talismane.machineLearning.features.DoubleFeature in project talismane by joliciel-informatique.

the class MaxentDetailedAnalysisWriter method onAnalyse.

/*
   * (non-Javadoc)
   * 
   * @see com.joliciel.talismane.maxent.MaxentObserver#onAnalyse(java.util.List,
   * java.util.Collection)
   */
@Override
public void onAnalyse(Object event, List<FeatureResult<?>> featureResults, Collection<Decision> outcomes) throws IOException {
    Map<String, Double> outcomeTotals = new TreeMap<String, Double>();
    double uniformPrior = Math.log(1 / (double) outcomeList.size());
    for (String outcome : outcomeList) outcomeTotals.put(outcome, uniformPrior);
    writer.append("####### Event: " + event.toString() + "\n");
    writer.append("### Feature results:\n");
    for (FeatureResult<?> featureResult : featureResults) {
        if (featureResult.getOutcome() instanceof List) {
            @SuppressWarnings("unchecked") FeatureResult<List<WeightedOutcome<String>>> stringCollectionResult = (FeatureResult<List<WeightedOutcome<String>>>) featureResult;
            for (WeightedOutcome<String> stringOutcome : stringCollectionResult.getOutcome()) {
                String featureName = featureResult.getTrainingName() + "|" + featureResult.getTrainingOutcome(stringOutcome.getOutcome());
                String featureOutcome = stringOutcome.getOutcome();
                double value = stringOutcome.getWeight();
                this.writeFeatureResult(featureName, featureOutcome, value, outcomeTotals);
            }
        } else {
            double value = 1.0;
            if (featureResult.getFeature() instanceof DoubleFeature) {
                value = (Double) featureResult.getOutcome();
            }
            this.writeFeatureResult(featureResult.getTrainingName(), featureResult.getOutcome().toString(), value, outcomeTotals);
        }
    }
    writer.append("### Outcome totals:\n");
    writer.append("# Uniform prior: " + uniformPrior + " (=1/" + outcomeList.size() + ")\n");
    double grandTotal = 0;
    for (String outcome : outcomeList) {
        double total = outcomeTotals.get(outcome);
        double expTotal = Math.exp(total);
        grandTotal += expTotal;
    }
    writer.append(String.format("%1$-30s", "outcome") + String.format("%1$#15s", "total(log)") + String.format("%1$#15s", "total") + String.format("%1$#15s", "normalised") + "\n");
    for (String outcome : outcomeList) {
        double total = outcomeTotals.get(outcome);
        double expTotal = Math.exp(total);
        writer.append(String.format("%1$-30s", outcome) + String.format("%1$#15s", decFormat.format(total)) + String.format("%1$#15s", decFormat.format(expTotal)) + String.format("%1$#15s", decFormat.format(expTotal / grandTotal)) + "\n");
    }
    writer.append("\n");
    Map<String, Double> outcomeWeights = new TreeMap<String, Double>();
    for (Decision decision : outcomes) {
        outcomeWeights.put(decision.getOutcome(), decision.getProbability());
    }
    writer.append("### Outcome list:\n");
    Set<WeightedOutcome<String>> weightedOutcomes = new TreeSet<WeightedOutcome<String>>();
    for (String outcome : outcomeList) {
        Double weightObj = outcomeWeights.get(outcome);
        double weight = (weightObj == null ? 0.0 : weightObj.doubleValue());
        WeightedOutcome<String> weightedOutcome = new WeightedOutcome<String>(outcome, weight);
        weightedOutcomes.add(weightedOutcome);
    }
    for (WeightedOutcome<String> weightedOutcome : weightedOutcomes) {
        writer.append(String.format("%1$-30s", weightedOutcome.getOutcome()) + String.format("%1$#15s", decFormat.format(weightedOutcome.getWeight())) + "\n");
    }
    writer.append("\n");
    writer.flush();
}

Example 2 with DoubleFeature

use of com.joliciel.talismane.machineLearning.features.DoubleFeature in project talismane by joliciel-informatique.

the class PerceptronDetailedAnalysisWriter method onAnalyse.

/*
   * (non-Javadoc)
   * 
   * @see com.joliciel.talismane.maxent.MaxentObserver#onAnalyse(java.util.List,
   * java.util.Collection)
   */
@Override
public void onAnalyse(Object event, List<FeatureResult<?>> featureResults, Collection<Decision> decisions) throws IOException {
    Map<String, Double> outcomeTotals = new TreeMap<String, Double>();
    for (String outcome : modelParams.getOutcomes()) outcomeTotals.put(outcome, 0.0);
    writer.append("####### Event: " + event.toString() + "\n");
    writer.append("### Feature results:\n");
    for (FeatureResult<?> featureResult : featureResults) {
        if (featureResult.getOutcome() instanceof List) {
            @SuppressWarnings("unchecked") FeatureResult<List<WeightedOutcome<String>>> stringCollectionResult = (FeatureResult<List<WeightedOutcome<String>>>) featureResult;
            for (WeightedOutcome<String> stringOutcome : stringCollectionResult.getOutcome()) {
                String featureName = featureResult.getTrainingName() + "|" + featureResult.getTrainingOutcome(stringOutcome.getOutcome());
                String featureOutcome = stringOutcome.getOutcome();
                double value = stringOutcome.getWeight();
                this.writeFeatureResult(featureName, featureOutcome, value, outcomeTotals);
            }
        } else {
            double value = 1.0;
            if (featureResult.getFeature() instanceof DoubleFeature) {
                value = (Double) featureResult.getOutcome();
            }
            this.writeFeatureResult(featureResult.getTrainingName(), featureResult.getOutcome().toString(), value, outcomeTotals);
        }
    }
    List<Integer> featureIndexList = new ArrayList<Integer>();
    List<Double> featureValueList = new ArrayList<Double>();
    modelParams.prepareData(featureResults, featureIndexList, featureValueList);
    double[] results = decisionMaker.predict(featureIndexList, featureValueList);
    writer.append("### Outcome totals:\n");
    writer.append(String.format("%1$-30s", "outcome") + String.format("%1$#15s", "total") + String.format("%1$#15s", "normalised") + "\n");
    int j = 0;
    for (String outcome : modelParams.getOutcomes()) {
        double total = outcomeTotals.get(outcome);
        double normalised = results[j++];
        writer.append(String.format("%1$-30s", outcome) + String.format("%1$#15s", decFormat.format(total)) + String.format("%1$#15s", decFormat.format(normalised)) + "\n");
    }
    writer.append("\n");
    Map<String, Double> outcomeWeights = new TreeMap<String, Double>();
    for (Decision decision : decisions) {
        outcomeWeights.put(decision.getOutcome(), decision.getProbability());
    }
    writer.append("### Outcome list:\n");
    Set<WeightedOutcome<String>> weightedOutcomes = new TreeSet<WeightedOutcome<String>>();
    for (String outcome : modelParams.getOutcomes()) {
        Double weightObj = outcomeWeights.get(outcome);
        double weight = (weightObj == null ? 0.0 : weightObj.doubleValue());
        WeightedOutcome<String> weightedOutcome = new WeightedOutcome<String>(outcome, weight);
        weightedOutcomes.add(weightedOutcome);
    }
    for (WeightedOutcome<String> weightedOutcome : weightedOutcomes) {
        writer.append(String.format("%1$-30s", weightedOutcome.getOutcome()) + String.format("%1$#15s", decFormat.format(weightedOutcome.getWeight())) + "\n");
    }
    writer.append("\n");
    writer.flush();
}